Offline Handwritten Signature Recognition System - Chavan, Harsha
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In this book, author proposed new offline handwritten signature Identification and Verification based on the contourlet coefficient as the feature extractor and Support Vector Machine (SVM) as the classifier. In projected method, first signature image is normalized based on size. After preprocessing, contourlet coefficients are computed on particular scale and direction using contourlet transform in feature extraction. After feature extraction, all extracted coefficients are feed to a layer of SVM classifiers as feature vector. The number of SVM classifiers is equal to the number of classes.…mehr

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Produktbeschreibung
In this book, author proposed new offline handwritten signature Identification and Verification based on the contourlet coefficient as the feature extractor and Support Vector Machine (SVM) as the classifier. In projected method, first signature image is normalized based on size. After preprocessing, contourlet coefficients are computed on particular scale and direction using contourlet transform in feature extraction. After feature extraction, all extracted coefficients are feed to a layer of SVM classifiers as feature vector. The number of SVM classifiers is equal to the number of classes. Each SVM classifier determines if the input image belongs to the resultant class or not. The main feature of proposed method is independency to nation of signers. The proposed methodology implemented using MATLAB R2009a software tool with image processing toolbox. The research is on English signature database, based on this experiment, we achieve a 94% identification rate.
Autorenporträt
Assistant professor in Computer Department from K.C.E College of Engineering and Technology Jalgaon, Ms Harsha completed her Masters from Bambhori. Her interests are Data Structure, Web Design, Image Processing, Computer Graphics and Multimedia. She is a life time member of ISTE and CSI.